1. Field of the Invention
The present invention relates to an object identification technique for identifying an object included in an input image.
2. Description of the Related Art
An object identification technique which identifies to which of categories specified by objects included in a plurality of images, which are registered in advance (registered images), an object included in an input image belongs by comparing the input image with the registered images is known. As an application field of the object identification technique, for example, a face object identification technique for specifying a person from a face object included in an input image is known.
In the face object identification technique, categories include names and IDs required to specify persons, and registered images are a plurality of face images (images including face objects) assigned names and IDs. In face object identification processing, registered images including images of the same person even under different photographing conditions have to be classified into a single category.
Note that “identification of an object” of terms used in this specification means determining difference between individuals (for example, a of a different of a person) included in an input image (that is, identification of a category to which an object belongs). “Detection of an object” means determination of objects which belong to a single category without discriminating individuals in association with objects included in an input image (for example, detection of face objects without specifying persons).
In the field of the object identification technique, a method of executing identification processing using feature amounts (partial feature amounts) in feature regions (partial feature regions) of a face object included in an image is generally known (for example, see Japanese Patent Laid-Open No. 2003-323622).
In this method, partial feature amounts are extracted in advance from some partial feature regions in registered images. When an input image to be identified is input, partial feature amounts are extracted from the partial feature regions in the input image, which correspond to those in the registered images. Then, scores obtained by comparing the partial feature amounts between the input image and registered images are integrated to calculate similarities between the input image and registered images. Furthermore, these processes are executed for respective categories, and the input image is classified into a category for which the highest similarity is finally calculated.
In case of the method of classifying an input image by identifying an object included in the input image using partial feature amounts in partial feature regions, the influences of photographing conditions can be effectively eliminated, thus improving an identification performance.
Note that partial feature regions used upon extracting partial feature amounts in such identification processing can be calculated in advance using a machine learning method such as an AdaBoost method disclosed in, for example, Shen, L., Bai, L., Bardsley, D., and Wang, Y., “Gabor feature selection for face recognition using improved adaboost learning.” (Proceedings of International Workshop on Biometric Recognition System, in conjunction with ICCV '05, 2005). Furthermore, as a technique suited to such prior learning, for example, a sequential learning method which improves an identification performance by sequentially adding learning data during operation of identification processing is prevalent (for example, see Glenn Fung and O. L. Mangasarian, “Incremental support vector machine classification” (Proceedings of Second SIAM International Conference on Data Mining, 2002)).
In the field of the face object identification technique, it is important to cover variations of the appearance of a face object due to different photographing conditions, as described above, and also to attain accurate identification by adequately judging even small differences between similar categories.
More specifically, upon identification of a face object, it is indispensable to be free from the influences of photographing condition changes such as face directions, face expressions, and illumination states independently of races, ages, and genders. In addition, it is important to adequately identify a face object between very similar face objects such as a parent and child, and brothers.
However, when the method disclosed in the prior art reference is applied, although the influences of the photographing conditions can be eliminated, a sufficient identification performance cannot be obtained for two categories having very small differences (for example, for categories of twins). This is because the aforementioned method is configured for the purpose of improving the identification performance of a plurality of categories as a whole, but it is not configured in recognizing improvement of an identification performance between two specific categories.
On the other hand, in case of identification of a face object, it is important to adequately identify a face object between very similar face objects. That is, in face object identification, it is desirable to obtain a sufficient identification performance even for two categories having very small mutual differences while keeping high robustness against photographing condition changes.